Excess weight gain in the paediatric bleeding disorders population: Impact of the COVID‐19 Pandemic
Bibliographic record
Abstract
Abstract Introduction The COVID‐19 pandemic has resulted in lifestyle changes for children. The aim of this study was to evaluate the impact of the pandemic on weight/BMI in children with severe bleeding disorders. Methods We conducted a retrospective review of patients age 3–18 years with severe bleeding disorders on prophylactic therapy treated at SickKids Hospital (Toronto, Canada) between February 01, 2018 and March 31, 2021. We evaluated the following pre‐ and post‐COVID variables: weight (kg), weight percentile, BMI (kg/m2), BMI percentile, HJHS score, and prophylactic dosing (units/kg). Results One hundred and four patients were included in the final analysis. Diagnoses were as follows: haemophilia A (n = 92; 70.8%), haemophilia B (n = 17; 13.1%), type 3 von Willebrand disease (n = 11; 8.5%), the remainder were diagnosed with rare factor deficiencies. Median interval time from pre‐COVID measurements to latest follow‐up was 12.4 months (IQR 10.32–14.52 months) during which there was a statistically significant increase in median weight percentile +5.75 centiles (from 63rdcentile to 68.75thcentile). There was a statistically significant increase in mean BMI of +1.03 kg/m2(P = < .001) while median BMI percentile increased +8.82 centiles (from 53.9thcentile to 62.72ndcentile) and mean BMI percentile increased 3.42 centiles (from 57.5 centile to 60.9 centile). The group that gained the most weight centiles, BMI and BMI centiles were 5–14 years old. Conclusion There was a trend to weight gain over the study period. More long‐term data is required to evaluate the impact of this increase in weight and BMI on children with bleeding disorders.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".